{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 《零基础财务Python训练营》实操练习题\n",
    "## Day6 《数据可视化》"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**作业说明:**  为了帮助同学们掌握、巩固课程学习内容，并主动结合知识点思考问题逻辑，从而形成编程思维，我们为大家设计了2个不同难度的作业~\n",
    "\n",
    "▶基础题：针对当前章节传授知识点设计的题目，用于考察大家的学习掌握情况，同学们一定要尝试做出来哟~\n",
    "\n",
    "▶思考题：针对当前或近期所学内容设计的题目，用于将多个知识点串联、或是引发对后续学习内容的提前思考。同学们可能无法正确解答，但这些内容都是老师精心设计哒，目的是引发大家对知识点的深入探究~我们会有提示引导同学们思考的方向，相信这些思考会让大家不断提高自主探索的能力。\n",
    "\n",
    "★对于以上说明和作业问题，同学们可以在群里积极参与讨论或向助教老师咨询呦，相信大家只要认真完成作业，一定能够收获满满！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注意:**  本章节的可视化题目均采用最基本的图形操作代码（题目要求中会给出），主要目的在于引导同学们对图表展示有深入的思考\n",
    "<br>大家也可以根据课上的内容，尝试设置x轴、y轴、title、调节参数、样式等操作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.【基础题】 渠道推荐用户数可视化分析（拓展题运行结果截屏打卡）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.1 数据处理和分析回顾\n",
    "<br>选取了四个渠道推荐用户数调查表，按照要求先进行简单的数据处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（1）在python中读取作业中的excel文件，DataFrame的变量名要求为\"data1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#请在此输入答案并运行#\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（2）使用head()函数查看数据情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>时间</th>\n",
       "      <th>渠道A推荐用户数</th>\n",
       "      <th>渠道B推荐用户数</th>\n",
       "      <th>渠道C推荐用户数</th>\n",
       "      <th>渠道D推荐用户数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>938</td>\n",
       "      <td>542</td>\n",
       "      <td>570</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>1423</td>\n",
       "      <td>1067</td>\n",
       "      <td>521</td>\n",
       "      <td>392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>1333</td>\n",
       "      <td>166</td>\n",
       "      <td>557</td>\n",
       "      <td>344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>1261</td>\n",
       "      <td>258</td>\n",
       "      <td>537</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>1198</td>\n",
       "      <td>805</td>\n",
       "      <td>503</td>\n",
       "      <td>556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          时间  渠道A推荐用户数  渠道B推荐用户数  渠道C推荐用户数  渠道D推荐用户数\n",
       "0 2020-01-01       938       542       570       133\n",
       "1 2020-01-02      1423      1067       521       392\n",
       "2 2020-01-03      1333       166       557       344\n",
       "3 2020-01-04      1261       258       537       231\n",
       "4 2020-01-05      1198       805       503       556"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#请在此输入答案并运行#\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（3）此时我们想截取2020年第一季度的数据进行分析，DataFrame的变量名要求为\"data2\"\n",
    " <br><font color=#3a3947 size=2> 提示：使用iloc[]进行数据获取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>时间</th>\n",
       "      <th>渠道A推荐用户数</th>\n",
       "      <th>渠道B推荐用户数</th>\n",
       "      <th>渠道C推荐用户数</th>\n",
       "      <th>渠道D推荐用户数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>938</td>\n",
       "      <td>542</td>\n",
       "      <td>570</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>1423</td>\n",
       "      <td>1067</td>\n",
       "      <td>521</td>\n",
       "      <td>392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>1333</td>\n",
       "      <td>166</td>\n",
       "      <td>557</td>\n",
       "      <td>344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>1261</td>\n",
       "      <td>258</td>\n",
       "      <td>537</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>1198</td>\n",
       "      <td>805</td>\n",
       "      <td>503</td>\n",
       "      <td>556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>888</td>\n",
       "      <td>671</td>\n",
       "      <td>551</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>2020-03-28</td>\n",
       "      <td>607</td>\n",
       "      <td>182</td>\n",
       "      <td>558</td>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>2020-03-29</td>\n",
       "      <td>1412</td>\n",
       "      <td>618</td>\n",
       "      <td>547</td>\n",
       "      <td>449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>959</td>\n",
       "      <td>830</td>\n",
       "      <td>596</td>\n",
       "      <td>551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>1387</td>\n",
       "      <td>212</td>\n",
       "      <td>593</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>91 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           时间  渠道A推荐用户数  渠道B推荐用户数  渠道C推荐用户数  渠道D推荐用户数\n",
       "0  2020-01-01       938       542       570       133\n",
       "1  2020-01-02      1423      1067       521       392\n",
       "2  2020-01-03      1333       166       557       344\n",
       "3  2020-01-04      1261       258       537       231\n",
       "4  2020-01-05      1198       805       503       556\n",
       "..        ...       ...       ...       ...       ...\n",
       "86 2020-03-27       888       671       551       253\n",
       "87 2020-03-28       607       182       558       311\n",
       "88 2020-03-29      1412       618       547       449\n",
       "89 2020-03-30       959       830       596       551\n",
       "90 2020-03-31      1387       212       593       335\n",
       "\n",
       "[91 rows x 5 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#请在此输入答案并运行#\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.2 数据可视化\n",
    "<br>按照要求绘制2020年第一季度各渠道推荐用户数的相应图形"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(1) 导入Matplotlib库，并进行基本设置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#请在此输入答案并运行#\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(2) 使用data.column.plot(kind='bar ')绘制出渠道A推荐用户数的条形图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#请在此输入答案并运行#\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(3) 使用data.column.plot(kind='line ')绘制出渠道B推荐用户数的折线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#请在此输入答案并运行#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(4) 使用data.column.hist() 绘制出渠道C推荐用户数的直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#请在此输入答案并运行#\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(5) 使用data.column.plot.kde() 绘制出渠道D推荐用户数的密度图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#请在此输入答案并运行#\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(6) 求出四个渠道分别推荐用户数总和,并绘制出显示各渠道占比的饼图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#请在此输入答案并运行#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(7) 拓展：使用for循环和iloc[]函数在一张图中绘制出四个渠道推荐用户数的折线图\n",
    "<br>完成此拓展题截屏打卡（代码和结果）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=#A64646 size=2> **同学们真棒，看来大家已经掌握数据可视化基本内容啦！**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.【思考题】 上海银行股票数据分析（拓展题运行结果截屏打卡）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.1 数据导入和查看"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（1）在python中读取作业中的csv文件，DataFrame的变量名要求为\"data3\",并使用head函数进行数据预览"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#请在此输入答案并运行#\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（2）导入相关数据库matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#请在此输入答案并运行#\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.2 数据可视化展示\n",
    "<br>该可视化展示同第一题中的操作，不过这里会有更进一步的处理和思考呦"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "拓展：将数据的索引列重置为时间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Adj close</th>\n",
       "      <th>Exchange Rate</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Transaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-03-31</td>\n",
       "      <td>8.79</td>\n",
       "      <td>8.82</td>\n",
       "      <td>8.64</td>\n",
       "      <td>8.75</td>\n",
       "      <td>8.75</td>\n",
       "      <td>0.3240</td>\n",
       "      <td>44404256</td>\n",
       "      <td>388013111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-03-30</td>\n",
       "      <td>8.75</td>\n",
       "      <td>8.78</td>\n",
       "      <td>8.70</td>\n",
       "      <td>8.74</td>\n",
       "      <td>8.77</td>\n",
       "      <td>0.1927</td>\n",
       "      <td>26400734</td>\n",
       "      <td>230602730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-03-29</td>\n",
       "      <td>8.77</td>\n",
       "      <td>8.79</td>\n",
       "      <td>8.57</td>\n",
       "      <td>8.64</td>\n",
       "      <td>8.66</td>\n",
       "      <td>0.3619</td>\n",
       "      <td>49587936</td>\n",
       "      <td>431956026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-03-26</td>\n",
       "      <td>8.66</td>\n",
       "      <td>8.68</td>\n",
       "      <td>8.54</td>\n",
       "      <td>8.58</td>\n",
       "      <td>8.57</td>\n",
       "      <td>0.1995</td>\n",
       "      <td>27341910</td>\n",
       "      <td>235435272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-03-25</td>\n",
       "      <td>8.57</td>\n",
       "      <td>8.69</td>\n",
       "      <td>8.51</td>\n",
       "      <td>8.60</td>\n",
       "      <td>8.64</td>\n",
       "      <td>0.1940</td>\n",
       "      <td>26584837</td>\n",
       "      <td>227865109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1027</th>\n",
       "      <td>2017-01-09</td>\n",
       "      <td>23.24</td>\n",
       "      <td>23.30</td>\n",
       "      <td>23.08</td>\n",
       "      <td>23.12</td>\n",
       "      <td>23.16</td>\n",
       "      <td>2.0287</td>\n",
       "      <td>12181330</td>\n",
       "      <td>283000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1028</th>\n",
       "      <td>2017-01-06</td>\n",
       "      <td>23.16</td>\n",
       "      <td>23.56</td>\n",
       "      <td>23.12</td>\n",
       "      <td>23.45</td>\n",
       "      <td>23.50</td>\n",
       "      <td>3.4872</td>\n",
       "      <td>20938854</td>\n",
       "      <td>488000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1029</th>\n",
       "      <td>2017-01-05</td>\n",
       "      <td>23.50</td>\n",
       "      <td>23.76</td>\n",
       "      <td>23.47</td>\n",
       "      <td>23.74</td>\n",
       "      <td>23.72</td>\n",
       "      <td>2.9977</td>\n",
       "      <td>17999621</td>\n",
       "      <td>424000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030</th>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>23.72</td>\n",
       "      <td>23.80</td>\n",
       "      <td>23.55</td>\n",
       "      <td>23.59</td>\n",
       "      <td>23.59</td>\n",
       "      <td>2.9528</td>\n",
       "      <td>17730203</td>\n",
       "      <td>420000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031</th>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>23.59</td>\n",
       "      <td>23.78</td>\n",
       "      <td>23.31</td>\n",
       "      <td>23.33</td>\n",
       "      <td>23.28</td>\n",
       "      <td>3.4383</td>\n",
       "      <td>20644985</td>\n",
       "      <td>487000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1032 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date  Close   High    Low   Open  Adj close  Exchange Rate  \\\n",
       "0    2021-03-31   8.79   8.82   8.64   8.75       8.75         0.3240   \n",
       "1    2021-03-30   8.75   8.78   8.70   8.74       8.77         0.1927   \n",
       "2    2021-03-29   8.77   8.79   8.57   8.64       8.66         0.3619   \n",
       "3    2021-03-26   8.66   8.68   8.54   8.58       8.57         0.1995   \n",
       "4    2021-03-25   8.57   8.69   8.51   8.60       8.64         0.1940   \n",
       "...         ...    ...    ...    ...    ...        ...            ...   \n",
       "1027 2017-01-09  23.24  23.30  23.08  23.12      23.16         2.0287   \n",
       "1028 2017-01-06  23.16  23.56  23.12  23.45      23.50         3.4872   \n",
       "1029 2017-01-05  23.50  23.76  23.47  23.74      23.72         2.9977   \n",
       "1030 2017-01-04  23.72  23.80  23.55  23.59      23.59         2.9528   \n",
       "1031 2017-01-03  23.59  23.78  23.31  23.33      23.28         3.4383   \n",
       "\n",
       "        Volume  Transaction  \n",
       "0     44404256    388013111  \n",
       "1     26400734    230602730  \n",
       "2     49587936    431956026  \n",
       "3     27341910    235435272  \n",
       "4     26584837    227865109  \n",
       "...        ...          ...  \n",
       "1027  12181330    283000000  \n",
       "1028  20938854    488000000  \n",
       "1029  17999621    424000000  \n",
       "1030  17730203    420000000  \n",
       "1031  20644985    487000000  \n",
       "\n",
       "[1032 rows x 9 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#处理“Date”序列，转化为标准的时间格式\n",
    "data3['Date'] = pd.to_datetime(data3['Date'],format='%Y-%m-%d')\n",
    "\n",
    "data3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(1)将转换后的Date设为新的索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#请在此输入答案并运行#\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(2)使用data.column.plot(kind=' '),绘制列为\"Close\"的折线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#请在此输入答案并运行#\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(3)用同样的方法,做出剩余列（'High','Low','Open','Adj close','Exchange Rate','Volume','Transaction'）的折线图（大家可以挑选几列进行尝试）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#请在此输入答案并运行#\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=#A64646 size=2> **对于这些有时间变化趋势的变量，用折线图可以清楚的看出数据的变动情况。对此，我们是否有简便的方法一次性输出所有列的图形呢？**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(4)拓展题：结合for循环语句，输入代码后可直接显示所有列的折线图\n",
    "<br>完成此拓展题截屏打卡（代码和结果）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=#A64646 size=2> **快动手试试吧！不要忘记截图打卡呦**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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